Neural Machine Unranking

Summary

This paper introduces neural machine unranking (NuMuR), a task of removing selected data from neural information retrieval systems, motivated by data privacy compliance. The authors propose CoCoL, a dual-objective framework combining a contrastive loss that lowers relevance scores on forget sets with a consistent loss that preserves accuracy on retain sets. Experiments on two datasets and four neural IR models show substantial forgetting with minimal retention and generalization loss.